This invention relates to multimedia items, such as music, movies, fashions, books, television shows and other entertainment choices, and to methods and apparatus for receiving inputs from a user and generating media playlists or lists that determine the order in which media are presented to a consumer. One area where the generation of playlists is of particular current interest is music playlists. In previous decades, a music listener's music collection was pre-organized onto albums and tapes. When a listener wanted to listen to a music selection, they would select an album that included the selection, cue the album and listen to it. Today, however, music collections can consist of hundreds, thousands, or even millions of individual songs stored as individual files on a computer. Selecting and organizing subsets of these songs into “playlists”, that is, an ordered list of songs is an essential task of the music listener. However this task is tedious, especially when the music collection is large and diverse.
Consequently, various methods have been devised in order to automatically generate media playlists from a media collection. These methods generally strive to possess the following characteristics:                they are relatively automatic and require little or no consumer intervention;        they generate playlists that contain only media selections that a consumer wants to review and do not contain “bad” selections that the consumer does not want to review;        they generate playlists that include a mix of old “favorite” selections as well as new (to the consumer) selections. The optimum ratio of old to new media selections varies from consumer to consumer; and        they generate playlists that contain a variety of media styles. Again, the optimum range of variety varies from consumer to consumer. For instance, with song selections, one listener may enjoy a variety of music ranging from 70's classic rock to big band jazz while another listener may enjoy the narrow range that encompasses “thrash” and “death metal” music.        
A number of conventional strategies have been developed that automate the generation of playlists. For example, one conventional method is called the “shuffle play” method. This method (recently touted with the “iPod shuffle” product being sold by Apple Computer Corporation) generates a playlist by randomizing the reviewing order of an entire collection of media. This technique works fine for small media collections, but does not scale well. For example, using shuffle play with a music collection of a million songs will result in a playlist that contains few (if any) songs of interest to a listener. Thus, with large media collections, the shuffle play method generates playlists where ratio of “bad” selections to total selections approaches one.
Another conventional technique is called “recently played.” This technique populates a playlist with media selections that have been recently reviewed by the consumer. It ensures that the playlist contains only media selections familiar (and presumably appealing) to the consumer. However playlists generated with this technique will never contain new media selections or selections unfamiliar to the consumer. For many consumers, exposure to new selections is very important. This method eliminates that possibility.
Still another prior art technique is called “similar to.” This technique populates a playlist with selections that are “similar to” an initial seed selection or seed selection set. Similarity is based on the type of media, for example, in the case of music, acoustical similarity may be used to select songs. This technique will allow new selections to appear in the playlist while at the same time restricting the playlist to selections that sound like a particular selection or selection set which the consumer enjoys. The “similar-to” technique”—reduces the number of “bad” selections and “no new selections” problems discussed previously but suffers from the lack of variety. All selections in the playlist will appear similar to the consumer.